Iterative Active-Inactive Obstacle Classification for Time-Optimal Collision Avoidance

TL;DR

An iterative active-inactive obstacle approach, which involves identifying a subset of the obstacles as 'active' on the basis of previous findings derived from prior iterations, allows for a more efficient calculation of the optimal path by reducing the number of obstacles that need to be considered.

Abstract

Time-optimal obstacle avoidance is a prevalent problem encountered in various fields, including robotics and autonomous vehicles, where the task involves determining a path for a moving vehicle to reach its goal while navigating around obstacles within its environment. This problem becomes increasingly challenging as the number of obstacles in the environment rises. We propose an iterative active-inactive obstacle approach, which involves identifying a subset of the obstacles as "active", that considers solely the effect of the "active" obstacles on the path of the moving vehicle. The remaining obstacles are considered "inactive" and are not considered in the path planning process. The obstacles are classified as 'active' on the basis of previous findings derived from prior iterations. This approach allows for a more efficient calculation of the optimal path by reducing the number of obstacles that need to be considered. The effectiveness of the proposed method is demonstrated with two different dynamic models using the various number of obstacles. The results show that the proposed method is able to find the optimal path in a timely manner, while also being able to handle a large number of obstacles in the environment and the constraints on the motion of the object.

Figures (3)

• Figure 1: Visualization of the proposed method. The obstacles in the scenario are categorized into two distinct sets: the 'active' obstacles, depicted in black, and the 'inactive' obstacles, represented in orange. The optimization problem, as defined in \ref{['objective']}, is exclusively addressed with respect to the 'active' obstacles. Consequently, the resulting solution, denoted by the blue line, is derived solely from the constraints imposed by the 'active' obstacles.
• Figure 2: The distribution of the number of active obstacles in the solution for different numbers of obstacles in the quadrotor scenarios.
• Figure 3: The findings pertain to a series of experimental scenarios involving 100 obstacles, within which the solution set encompasses 20 active obstacles. Notably, inactive obstacles are represented by the color orange, while active obstacles are denoted by the color black. The trajectory is illustrated as a blue line, with the initial point marked as '+' and the final point indicated by '*'. It is noteworthy that the longest consecutive solution observed in this study comprises eight iterative steps. Additionally, it is imperative to emphasize that each successive solution, with the exception of the final one, introduces a novel set of active obstacles to the ongoing optimization problem.